Traditional online analytical processing (OLAP) systems process records in a fact table and summarize their key statistics with respect to certain measure attributes. A user can select a set of dimension attributes and their corresponding levels of abstraction and an OLAP system will partition the data records based on those dimension attributes and abstraction levels. Records that share the same values in those dimension attributes (with respect to the selected abstraction levels) are grouped together. Aggregate functions (such as sum, average, count) are then applied to the measure attributes of the records in each group. Next, an OLAP system reports a summary (referred to as a cuboid) by tabulating the aggregate values for all possible groups. OLAP is a powerful data analysis tool because it allows users to navigate or explore different levels of summarization by interactively changing the set of dimension attributes and their abstraction levels. In other words, users can navigate from one cuboid to another interactively in order to obtain the most interesting statistics through a set of pre-defined OLAP operations, for example, roll-up, drill-down, slice, and dice.
Although powerful existing OLAP systems only handle independent records, many kinds of real-life data exhibit logical ordering among their data items and are thus sequential in nature. Examples of sequence data include stock market data, web server access logs and RFID logs such as those generated by a commodity tracking system in a supply chain. Similar to conventional data, there is a strong demand to warehouse and to analyze the vast amount of sequence data in a user-friendly and efficient way. However, traditional online analytical processing (OLAP) systems and techniques are not designed for sequence data and they are incapable of supporting sequence data analysis.
Sequence databases and OLAP do not address the issues of sequence data analysis as well. OLAP on unconventional data does not address the problem of pattern based grouping and analysis.